What is Google Gemini?

What exactly is Gemini?

Gemini is Google's answer to generative AI: a model family designed to be multimodal from the ground up. Rather than handling text alone, Gemini understands and generates language, images, audio, video and code within the same model. The name refers at once to the underlying model, the chat app (formerly Bard) and the AI features Google embeds across its products.

Gemini is developed by Google DeepMind. Google updates the models in rapid generations (such as Gemini 1.5, 2.0 and newer releases); exact version numbers, context lengths and prices change frequently. For binding details, always consult Google's current documentation.

Model family and capabilities

• Multimodal: handles text, image, audio, video and code within a single model.
• Model sizes: powerful Pro models, fast and cost-efficient Flash models, and Nano for on-device execution.
• Long context window: newer Gemini versions can process very large volumes of documents, reaching into the millions of tokens at once.
• Tools: connection to Google Search for up-to-date information, code execution and function calling.
• Access: available through the Gemini app, the Gemini API, Google AI Studio and the Vertex AI cloud platform.

The Google integration

The biggest difference from standalone AI services is how deeply Gemini is embedded in the Google ecosystem. It appears where many people and organisations already work.

• Google Workspace: AI assistance in Gmail, Docs, Sheets, Slides and Meet for drafts, summaries and notes.
• Android and Pixel: Gemini can replace Google Assistant as the voice and system assistant.
• Chrome and Google Search: AI Overviews and assistance features in the browser.
• Google Cloud / Vertex AI: platform to integrate Gemini into your own enterprise applications, including a choice of data region.
• Gemini API and AI Studio: for developers to prototype quickly and run in production.

Business use cases

Actual value depends heavily on the Workspace or Cloud licence. Feature scope, languages and availability vary by plan and region; a pilot with clear metrics is wiser than a blanket rollout without testing.

• Summarise documents, draft reports and emails, and generate meeting notes automatically.
• Research and knowledge management across large document sets, thanks to the long context window.
• Customer-service assistants and internal chatbots built on Vertex AI.
• Data analysis and spreadsheet support directly in Google Sheets.
• Software development: generate, explain, document and test code.

Limits and risks

• Hallucinations: Gemini can produce plausible but false statements; outputs must be verified.
• Knowledge cutoff: without web search, the model is limited to training data up to a certain date.
• Availability: features, languages and models differ by region and licence.
• Dependence on the Google ecosystem (vendor lock-in): switching providers can be costly.
• Reproducibility: answers vary; regulated processes need human review and controls.

Data protection and compliance in Switzerland and DACH

Anyone using Gemini for business in Switzerland must observe the revised Data Protection Act (revDSG/nFADP); the FDPIC (EDÖB) is the supervisory authority. For personal data, clarify the legal basis, transparency and data-processing arrangements. Where there is an EU nexus, the extraterritorial EU AI Act also applies.

• Distinguish consumer from enterprise editions: the free Gemini app may be handled differently from Workspace or Vertex AI contracts.
• Selectable data region: Google Cloud operates a region in Zurich, among others; check data residency and processing location contractually.
• Agree a data-processing agreement (DPA) and technical and organisational measures.
• Do not enter sensitive data without a clarified legal basis and approval.
• Consider sovereign alternatives such as the Swiss language model Apertus (ETH Zurich/EPFL) when data sovereignty is paramount.

How Gemini compares

Gemini competes with ChatGPT (OpenAI), Claude (Anthropic) and open models. Its strength lies less in any single benchmark than in integration: organisations already on Google Workspace or Google Cloud get AI directly within their existing workflow. The choice should rest on data protection, existing ecosystem, required features and cost — not marketing claims. A short comparison using your own real tasks is more telling than any vendor figure.